A DYNAMIC COMPOSITION OF ADAPTIVE COURSES BASED ON A MULTI-AGENT SYSTEM AND NEURAL NETWORKS

People learn in different ways, each according to his learning style. The research in the field of education have shown that students learn best when the training process adapts to their preferences and their learning styles.

Therefore, efforts must be combined and concentrated, both educationally and in terms of technology, to design appropriate learning environments respect the preferences and learning styles of learners.

In this paper we propose a Model Adaptive of E-Learning (MAEL) based on Semantic Web techniques and those of the Artificial Intelligence (AI), namely: ontologies, multi-agent system and neural network.

This model allows students, teachers and instructional designers to work with software agents to automatically and effectively build personalized learning paths guided by educational goals. In addition, it allows the learner to follow his training at their own pace and preferences, either individually or in collaboration with others (students or tutors).